A low energy chirp-pulsed radar thermography (CP-RT) was used as a nondestructive inspection technique to detect the subsurface delamination of CFRP composite. Laser intensities were modulated according to a chirp-pulsed radar signal, and the temperature rise is controlled within 2 Degree. The thermal-wave signal was processed by different post-processing characteristic extraction algorithms. These algorithms include time-frequency algorithms (Cross-correlation, CC, fast Fourier transform, FFT, and dual-orthogonal demodulation, DOD) and statistical analysis approaches (Principal component analysis, PCA and PCA based reconstructed independent component analysis, PCA-RICA). The SNRs of defects were employed to evaluate the defect detectability of different post processing algorithms. Two kind of 3D tomography methods were proposed. FFT phase presents a high SNR and has a good correlation with the depth of the defect. FFT based CP-RT has the potential to provide a rapid NDT&E and 3D tomography approach for CFRP with subsurface defects under low energy excitation condition.

Rolling bearings are essential transmission and support components in aircraft engines, playing a critical role in ensuring their safe and stable operation. Rolling bearing faults have a significant impact and should not be ignored. The effective diagnosis of bearing faults has always been a critical requirement for ensuring reliable operation. With the increasing demands of modern manufacturing to reduce costs and improve quality, the development of advanced bearing fault detection methods has become indispensable. This paper presents the brief review of recent trends in research on bearing failure modes, bearing fault detection and diagnosis methods, and development trends and prospects. This article provides a comprehensive review of the existing fault diagnosis methods for rolling bearings in four aspects: the integration of advanced sensor technology and advanced data processing technology, multimodal fusion, intelligent detection, and real-time monitoring. It discusses methods based on vibration analysis, acoustic methods, current-based methods, electromagnetic methods, infrared methods, radiographic methods, visual methods, and intelligent detection methods. This study reveals that the application of intelligent detection technology, multimodal fusion detection technology, and real-time online monitoring technology has achieved favorable results. In the future, bearing fault detection will develop in a more intelligent, integrated, and real-time direction.

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so‐called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine‐grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.